Why Greatness Cannot Be Planned: The Myth of the Objective
Kenneth O. Stanley & Joel Lehman
How to Prove It: A Structured Approach
Daniel J. Velleman
计算机网络: 自顶向下方法
James F. Kurose & Keith W. Ross
Proofs and Refutations: The Logic of Mathematical Discovery
Imre Lakatos
Matrix Calculus (for Machine Learning and Beyond)
Alan Edelman, Steven G. Johnson
Hyperparameter Optimization in Machine Learning
Luca Franceschi
Multi-Agent Reinforcement Learning: Foundations and Modern Approaches
Stefano V. Albrecht & Filippos Christianos & Lukas Schäfer
Physics-based Deep Learning
N. Thuerey, B. Holzschuh, P. Holl, G. Kohl, M. Lino, Q. Liu, ...
Non-Convex Optimization for Machine Learning
Prateek Jain & Purushottam Kar
Statistical Rethinking: A Bayesian Course With Examples in R ...
Richard McElreath
Probabilistic Graphical Models: Principles and Techniques
Daphne Koller & Nir Friedman
Computer Vision: Models, Learning, and Inference
Simon J. D. Prince
Pattern Recognition and Machine Learning
Christopher M. Bishop
Pen and Paper Exercises in Machine Learning
Michael U. Gutmann
Machine Learning, Second Edition: A Probabilistic Perspective
Kevin P. Murphy
Pattern Recognition and Machine Learning: Solutions to Exercises ...
Markus Svensén & Christopher M. Bishop